PyTorch For Deep Learning and Computer Vision

Colleagues, PyTorch is becoming a very transformative framework in the field of Deep Learning. Its flexibility has made building a Deep Learning model easier. This PyTorch For Deep Learning And Computer Vision teaches you Deep Learning with PyTorch. PyTorch has completely changed the landscape of the deep learning domain with its flexibility and has made building deep learning models easier. The development world offers some of the highest paying jobs in deep learning. Instructor Rayan Slim will help you learn and master deep learning with PyTorch. Having taught over 44,000 students, Rayan is a highly rated and experienced instructor who has followed a learning-by-doing style to create this course. By the end of this course, you will have built state-of-the-art deep learning and Computer Vision applications with PyTorch. You will learn the tensor data structure, machine and deep learning applications with PyTorch, neural networks from scratch, applied themes of advanced imagery and Computer Vision, complex problem solvings in Computer Vision by harnessing highly sophisticated pre-trained models and style transfer to build sophisticated AI applications. Skill-based training modules address: 1) Intro to Tensors, 2) Linear Regression, 3) Perceptrons, 4) Deep Neural Networks, 5) Image Recognition, 6) Convolutional Neural Networks, 7) CIFAR 10 Classification, 8) Transfer Learning, 9) Style Transfer – PyTorch plus two Appendices: Crash courses in Python and NumPy.

Enroll today (individuals & teams welcome): https://tinyurl.com/25bc4549 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Python Developer (Training)

Colleagues, this in-depth Python Developer training program will teach you the ins and outs of Python programming. You will learn to work with iPhone Notebook, the Collections Module, regular expressions, databases, CSV files, JSON, and XML. You will also learn advanced sorting, how to write object-oriented code in Python, and how to test and debug your Python code. Finally, you'll get a rapid introduction to NumPy, pandas, and matplotlib, which are Python libraries. Skill-based training modules include: 1) Introduction to Python-Python Basics, Functions and Modules, Math, Python Strings, Iterables: Sequences, Dictionaries, Sets, Virtual Environments, Packages, and pip, Flow Control, Exception Handling, Python Dates and Times, File Processing, PEP8 and Pylint, 2) Advanced Python-Advanced Python Concepts, Regular Expressions, Working with Data, Testing and Debugging, Classes and Objects, and 3) Python Data Analysis with JupyterLab-JupyterLab, NumPy, Pandas,.  

Enroll today (individuals & teams welcome): https://tinyurl.com/s8a54s2b 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Machine Learning Engineering Career Track (with a Mentor)

Colleagues, the Machine Learning Engineering Career Track Program will equip you to. Deploy ML Algorithms and build your own portfolio. More than 50% of the Springboard curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype and deploy a running application that can be accessed via API or web service.  The 500+ hour curriculum features a combination of videos, articles, hands-on projects, and career-related coursework.  Skill-based training modules include: 1) Battle-Tested Machine Learning Models, 2) Deep Learning, 3) Computer Vision and Image Processing, 4) The Machine Learning Engineering Stack, 5) ML Models At Scale and In Production, 6) Deploying ML Systems to Production, and 7) Working With Data. You will build a realistic, complete, ML application that’s available to use via an API, a web service or, optionally, a website. One-on-one Mentorship provides you with  weekly guided calls with your personal mentor, an industry expert. our career coaching calls will help your mentor. Create a successful job search strategy, Build your Machine Learning Engineering network, Find the right job titles and companies, Craft a Machine Learning Engineer resume and LinkedIn profile, Ace the job interview and Negotiate your salary.

Enroll today (individuals & teams welcome): https://fxo.co/BWBp  

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

TensorFlow 2.0 for Deep Learning (Training)

Colleagues, the TensorFlow 2.0 for Deep Learning program provides you with the core of deep learning using TensorFlow 2.0. You’ll learn to train your deep learning networks from scratch, pre-process and split your datasets, train deep learning models for real-world applications, and validate the accuracy of your models. By the end of the course, you’ll have a profound knowledge of how you can leverage TensorFlow 2.0 to build real-world applications without much effort. You will learn to Develop real-world deep learning applications, Classify IMDb Movie Reviews using Binary Classification Model, Build a model to classify news with multi-label, Train your deep learning model to predict house prices, Understand the whole package: prepare a dataset, build the deep learning model, and validate results, Assess the working of Recurrent Neural Networks and LSTM with hands-on examples, and Implement autoencoders and denoise autoencoders in a project to regenerate images. Skill-based training modules: 1) Deep Learning Basics, 2) TensorFlow 2.0 for Deep Learning, 3) Working with CNNs for Computer Vision and Deep Learning, 4) Working with LSTM for Text Data and Deep Learning, 5) Working with RNNs for Time Series Sequences and Deep Learning, 6) Autoencoders EAE and Denoising AE, and 7) Deep Learning Mini-Projects. This program uses a dedicated GitHub workspace.

Enroll today (individuals & teams welcome): https://tinyurl.com/4mu8fyfk 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy 

Advanced Predictive Techniques With Scikit-Learn & TensorFlow

Colleagues, the Advanced Predictive Techniques With Scikit-Learn And TensorFlow program will equip you to use ensemble methods to improve accuracy in classification and regression problems. Ensemble methods improve prediction accuracy by combining in a clever way predictions from many individual predictors. Artificial Neural Networks are models loosely based on how neural networks work in a living being. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library. Learn to use ensemble algorithms to combine many individual predictors to produce better predictions, apply advanced techniques such as dimensionality reduction to combine features and build better models, evaluate models and choose the optimal hyper-parameters using cross-validation, grasp the foundations for working and building models using Neural Networks and understand different techniques to solve problems that arise when doing Predictive Analytics in the real world. Training modules include: 1) Ensemble Methods for Regression and Classification, 2) Cross-Validation and Parameter Tuning, 3) Working with Features, 4) Introduction to Artificial Neural Networks and TensorFlow, and 5) Predictive Analytics with TensorFlow and Deep Neural Networks.  

Enroll today (individuals & teams welcome): https://tinyurl.com/4tyd7vjc 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Implementing Deep Learning Algorithms with TensorFlow 2.0

Colleagues, the Implementing Deep Learning Algorithms with TensorFlow 2.0 provides you with a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems. Learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You will program a model to classify breast cancer, predict stock market prices, process text as part of Natural Language Processing (NLP),  Skill-based training modules include: 1)  Deep Learning Introduction and Environment Setup, 2) Building First Neural Network for Tabular Data with TensorFlow 2.0, 3) Convolutional Neural Networks with TensorFlow 2.0, 4) Recurrent Neural Network with TensorFlow 2.0, 5) Long Short-Term Memory Networks (LSTM), and 6) Transfer Learning with TensorFlow 2.0.  Basic knowledge of Python is assumed. You will understand what problems Deep Learning and TensorFlow 2.0 have solved and can solve, various Deep Learning model architectures and work with them, apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification; predicting stock market data for Google; classifying Reuters news topics; and classifying flower species.

Enroll today (individuals & teams welcome): https://tinyurl.com/a9utjzxc 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Google Cloud Machine Learning with TensorFlow

Colleagues, the Google Cloud Machine Learning with TensorFlow equips you to use Google Cloud to train TensorFlow models and use them to predict results for multiple users. You will learn to efficiently train neural networks using large datasets and to serve your training models. You will use the power of Google's Cloud Platform to train deep neural networks faster. Learn to run predictions for your model using the cloud. You will also explore topics such as cloud infrastructures, distributed training, serverless technologies, model serving. Finally, you will learn to use GPUs organized in clusters to optimize your performance, train bigger models faster using the Google Cloud infrastructure, explore machine types and learn how to configure clusters to solve problems, train deep learning models using the Google Cloud AI Platform, run classical machine learning algorithms with TensorFlow, and execute your trained models to get predictions using the Artificial Intelligence Platform API. The program includes 40 lectures spanning the following training modules: 1) A Quick Start with Google Cloud Platform, 2) Machine Learning with TensorFlow Fundamentals, 3) Basic Model Training with TensorFlow 2.0, 4) Advanced Model Training with TensorFlow 2.0, 4) Serving Model Predictions with TensorFlow on GCP, and 6) Neural  Networks.

Enroll today (individuals & teams welcome): https://tinyurl.com/vj957fya 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Introduction to Deep Learning (and Neural Networks)

Colleagues, the Introduction to Deep Learning program will equip you in all the important concepts relating to deep learning models and how they give rise to the recent results in AI. We use guided examples and discuss a variety of practical applications, all accompanied by animations and visualizations. We also cover recent breakthroughs in deep learning research. This course will demystify the models that underpin the recent AI revolution and provide a solid foundation for further learning. Skill-based training modules include: 1) Fundamentals, 2) Perceptron: Weights, Biases, Activation Functions, 3) Multi-neuron Networks : XOR and nonlinearity, and 4)  Learning: Gradient Descent. After taking this course you will understand What deep learning is and how it  differs from other types of machine learning and artificial intelligence, How deep learning models use neural networks to make computations, What types of problems deep learning models can be used to solve, Types of data needed to train deep learning models, Variety of inputs deep  learning models receive and solutions they produce, Advantages that deep learning can offer over traditional machine learning, Why multi-neuron networks are able to solve complex problems, How neural networks use gradient descent and back-propagation to learn to make predictions.

Enroll today (individuals & teams welcome): https://tinyurl.com/phdve8xj 

Much career success, Lawrence E. Wilson - Online Learning Central

Unsupervised Machine Learning Projects with R

Colleagues, the Unsupervised Machine Learning Projects with R will help you build your knowledge and skills by guiding you in building machine learning projects with a practical approach and using the latest technologies provided by the R language such as Rmarkdown, R-shiny, and more. Also focus on two machine learning paradigms—K-Means Clustering and Principal Component Analysis—to grasp how they work and apply them to business Customer Segmentation (Market Segmentation Analysis). You will be equipped to deploy Machine Learning algorithms in R, explore K-means clustering techniques, prepare data for imputation and model diagnostics, train, evaluate, and improve your models, visualize the Principal Component Analysis model in 2D, perform pattern mining for transactional data, understand mocking  and how to use mocking frameworks and select design patterns. Skill-based training modules include:  1) Machine Learning Model in R, 2)  Exploring K-Means Clustering, 3) Principal Component Analysis (PCA), and 4) Pattern Mining.

Enroll today (individuals & teams welcome): https://tinyurl.com/2hxf3vzx 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Predictive Modeling and Machine Learning with MATLAB

ML colleagues, the Predictive Modeling and Machine Learning with MATLAB program will increase your ability to harness the power of MATLAB. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. You will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models deepening your skills in Machine  Learning, Matlab and Predictive Modeling. Training modules include: 1) Creating Regression Models: You will be introduced to the Supervised Machine Learning Workflow, creating and evaluating regression machine learning models (11 videos (Total 73 min), 7 readings, 7 quizzes), 2) Creating Classification Models: Grasp the basics of classification models. Train several types of classification models and evaluate the results 6 videos (Total 45 min), 6 readings, 2 quizzes, 3) Applying the Supervised Machine Learning Workflow: Complete supervised machine learning workflow. You'll use validation data to inform model creation. You'll apply different feature selection techniques to reduce model complexity. Then you will create ensemble models and optimize hyperparameters. At the end of the module, you'll apply these concepts to a final project. 9 videos (Total 49 min), 5 readings, 3 quizzes.

Enroll today (individuals & teams welcome): https://tinyurl.com/28ru79mx 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy